SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES
Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compare...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2609/2018/isprs-archives-XLII-3-2609-2018.pdf |
| _version_ | 1818257661369516032 |
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| author | R. Zhuo L. Xu J. Peng Y. Chen |
| author_facet | R. Zhuo L. Xu J. Peng Y. Chen |
| author_sort | R. Zhuo |
| collection | DOAJ |
| description | Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the “purified” pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of “purified” pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed “joint unmixing” approach provides more accurate endmember and abundance estimation results compared with “separate unmixing” approach. |
| first_indexed | 2024-12-12T17:47:12Z |
| format | Article |
| id | doaj.art-cce649a9c1a845b6b1089ea04b0ce2a2 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-12T17:47:12Z |
| publishDate | 2018-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-cce649a9c1a845b6b1089ea04b0ce2a22022-12-22T00:16:54ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-05-01XLII-32609261410.5194/isprs-archives-XLII-3-2609-2018SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGESR. Zhuo0L. Xu1J. Peng2Y. Chen3Dept. of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaDept. of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaDept. of Land Science and Technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaLand Consolidation and Rehabilitation Center, Ministry of Land and Resource, Beijing, ChinaTemporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied. Nevertheless, using the temporal information can provide improved unmixing performance when compared to independent image analyses. Moreover, different land cover types may demonstrate different temporal patterns, which can aid the discrimination of different natures. Therefore, this letter presents time series K-P-Means, a new solution to the problem of unmixing time series Landsat imagery. The proposed approach is to obtain the “purified” pixels in order to achieve optimal unmixing performance. The vertex component analysis (VCA) is used to extract endmembers for endmember initialization. First, nonnegative least square (NNLS) is used to estimate abundance maps by using the endmember. Then, the estimated endmember is the mean value of “purified” pixels, which is the residual of the mixed pixel after excluding the contribution of all nondominant endmembers. Assembling two main steps (abundance estimation and endmember update) into the iterative optimization framework generates the complete algorithm. Experiments using both simulated and real Landsat 8 images show that the proposed “joint unmixing” approach provides more accurate endmember and abundance estimation results compared with “separate unmixing” approach.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2609/2018/isprs-archives-XLII-3-2609-2018.pdf |
| spellingShingle | R. Zhuo L. Xu J. Peng Y. Chen SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES |
| title_full | SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES |
| title_fullStr | SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES |
| title_full_unstemmed | SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES |
| title_short | SPECTRAL UNMIXING ANALYSIS OF TIME SERIES LANDSAT 8 IMAGES |
| title_sort | spectral unmixing analysis of time series landsat 8 images |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2609/2018/isprs-archives-XLII-3-2609-2018.pdf |
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